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The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis

This paper uses an infrared high-speed motion capture system based on deep learning to analyze difficult movements, which helps aerobics athletes master difficult movements more accurately. Firstly, changes in joint angle, speed of movement, and ground pressure are used to analyze the impact and rol...

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Detalles Bibliográficos
Autores principales: Qiu, Yaoyu, Guan, Yingrong, Liu, Shuang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212154/
https://www.ncbi.nlm.nih.gov/pubmed/37228162
http://dx.doi.org/10.1371/journal.pone.0286313
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author Qiu, Yaoyu
Guan, Yingrong
Liu, Shuang
author_facet Qiu, Yaoyu
Guan, Yingrong
Liu, Shuang
author_sort Qiu, Yaoyu
collection PubMed
description This paper uses an infrared high-speed motion capture system based on deep learning to analyze difficult movements, which helps aerobics athletes master difficult movements more accurately. Firstly, changes in joint angle, speed of movement, and ground pressure are used to analyze the impact and role of motion fluency and completion based on a biomechanical perspective. Moreover, based on the existing infrared high-speed motion capture systems, the Restricted Boltzmann Machine (RBM) model is introduced to construct an unsupervised similarity framework model. Next, the motion data is reorganized based on three-dimensional information to adapt to the model’s input. Then, the framework performs similar frame matching to obtain a set of candidate frames that can be used as motion graph nodes. After the infrared high-speed motion capture system and inertial sensors are simultaneously applied to subjects, the multi-correlation coefficients (CMC) values of the hip, knee, and ankle angles are 0.94 ± 0.06, 0.98 ± 0.01, and 0.87 ± 0.09, respectively. The two systems show a high degree of correlation in the measurement results, and the knee joint is the most significant correlation. Finally, a motion graph is constructed to control its trajectory and adjust its motion pattern. The infrared high-speed motion capture system optimized for deep learning can extract features from human bone data and capture motion more accurately, helping trainers to fully understand difficult movements.
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spelling pubmed-102121542023-05-26 The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis Qiu, Yaoyu Guan, Yingrong Liu, Shuang PLoS One Research Article This paper uses an infrared high-speed motion capture system based on deep learning to analyze difficult movements, which helps aerobics athletes master difficult movements more accurately. Firstly, changes in joint angle, speed of movement, and ground pressure are used to analyze the impact and role of motion fluency and completion based on a biomechanical perspective. Moreover, based on the existing infrared high-speed motion capture systems, the Restricted Boltzmann Machine (RBM) model is introduced to construct an unsupervised similarity framework model. Next, the motion data is reorganized based on three-dimensional information to adapt to the model’s input. Then, the framework performs similar frame matching to obtain a set of candidate frames that can be used as motion graph nodes. After the infrared high-speed motion capture system and inertial sensors are simultaneously applied to subjects, the multi-correlation coefficients (CMC) values of the hip, knee, and ankle angles are 0.94 ± 0.06, 0.98 ± 0.01, and 0.87 ± 0.09, respectively. The two systems show a high degree of correlation in the measurement results, and the knee joint is the most significant correlation. Finally, a motion graph is constructed to control its trajectory and adjust its motion pattern. The infrared high-speed motion capture system optimized for deep learning can extract features from human bone data and capture motion more accurately, helping trainers to fully understand difficult movements. Public Library of Science 2023-05-25 /pmc/articles/PMC10212154/ /pubmed/37228162 http://dx.doi.org/10.1371/journal.pone.0286313 Text en © 2023 Qiu et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Qiu, Yaoyu
Guan, Yingrong
Liu, Shuang
The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
title The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
title_full The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
title_fullStr The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
title_full_unstemmed The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
title_short The analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
title_sort analysis of infrared high-speed motion capture system on motion aesthetics of aerobics athletes under biomechanics analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10212154/
https://www.ncbi.nlm.nih.gov/pubmed/37228162
http://dx.doi.org/10.1371/journal.pone.0286313
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